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Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications

Dr. Mohammad Abdur Razzaque

Md. Rezaul Karim

€28.99

4 (1 Ratings)

Paperback Jun 2019 308 pages 1st Edition

Dr. Mohammad Abdur Razzaque

Md. Rezaul Karim

€28.99

4 (1 Ratings)

Paperback Jun 2019 308 pages 1st Edition

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Key benefits

Understand how deep learning facilitates fast and accurate analytics in IoT
Build intelligent voice and speech recognition apps in TensorFlow and Chainer
Analyze IoT data for making automated decisions and efficient predictions

Description

Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.

Who is this book for?

If you’re an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.

What you will learn

Get acquainted with different neural network architectures and their suitability in IoT
Understand how deep learning can improve the predictive power in your IoT solutions
Capture and process streaming data for predictive maintenance
Select optimal frameworks for image recognition and indoor localization
Analyze voice data for speech recognition in IoT applications
Develop deep learning-based IoT solutions for healthcare
Enhance security in your IoT solutions
Visualize analyzed data to uncover insights and perform accurate predictions

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What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Estimated delivery fee Deliver to Ireland

Premium delivery 7 - 10 business days

€23.95

(Includes tracking information)

Product Details

Publication date : Jun 27, 2019

Length: 308 pages

Edition : 1st

Language : English

ISBN-13 : 9781789616132

Category :

Data

Languages :

Python

Concepts :

Deep Learning

Tools :

TensorFlow

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14 Chapters

Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks

The End-to-End Life Cycle of the IoT

The E2E life cycle of the IoT

IoT application domains

The importance of analytics in IoT

The motivation to use DL in IoT data analytics

The key characteristics and requirements of IoT data

Summary

Reference

Deep Learning Architectures for IoT

A soft introduction to ML

ML tasks

Delving into DL

Artificial neural networks

Neural network architectures

DL frameworks and cloud platforms for IoT

Summary

Section 2: Hands-On Deep Learning Application Development for IoT

Image Recognition in IoT

IoT applications and image recognition

Use case one – image-based automated fault detection

Use case two – image-based smart solid waste separation

Transfer learning for image recognition in IoT

CNNs for image recognition in IoT applications

Collecting data for use case one

Collecting data for use case two

Audio/Speech/Voice Recognition in IoT

Speech/voice recognition for IoT

Use case one – voice-controlled smart light

Use case two – voice-controlled home access

DL for sound/audio recognition in IoT

CNNs and transfer learning for speech recognition in IoT applications

Indoor Localization in IoT

An overview of indoor localization

DL-based indoor localization for IoT

Example – Indoor localization with Wi-Fi fingerprinting

Deployment techniques

Summary

Physiological and Psychological State Detection in IoT

IoT-based human physiological and psychological state detection

Use case one – remote progress monitoring of physiotherapy

Use case two — IoT-based smart classroom

Deep learning for human activity and emotion detection in IoT

LSTM, CNNs, and transfer learning for HAR/FER in IoT applications

IoT Security

Security attacks in IoT and detections

Use case one: intelligent host intrusion detection in IoT

Use case two: traffic-based intelligent network intrusion detection in IoT

DL for IoT security incident detection

Section 3: Advanced Aspects and Analytics in IoT

Predictive Maintenance for IoT

Predictive maintenance for IoT

Example – PM for an aircraft gas turbine engine

DL for predicting RLU

FAQs

Summary

Deep Learning in Healthcare IoT

IoT in healthcare

Use case one – remote management of chronic disease

Use case two – IoT for acne detection and care

What's Next - Wrapping Up and Future Directions

What we have covered in this book?

Deployment challenges of DL solutions in resource-constrained IoT devices

Existing solutions to support DL in resource-constrained IoT devices

Potential future solutions

Summary

References

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Customer reviews

Rating distribution

(1 Ratings)

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4 star 100%

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1 star 0%

Amazon Customer Sep 29, 2019

This is a very helpful book for the beginners in Internet Of Things. It helped me a lot in understanding the basics. This is a must have book for everyone who wants to learn IoT.

Amazon Verified review

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About the authors

Dr. Mohammad Abdur Razzaque

Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).

See other products by Dr. Mohammad Abdur Razzaque

Md. Rezaul Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

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Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications

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